The road freight transport sector is one of the largest contributors to carbon emissions. To address this issue and reduce both carbon emissions and fuel consumption, the road transportation system is undergoing a significant transformation with the development of autonomous truck platoons (ATPs). Despite the promising potential for large-scale deployment of ATPs and the substantial number of human-driven heavy-duty trucks currently in operation, research on the lateral interactions between truck platoons-whether human-driven or automated-and surrounding passenger cars remains limited. Given the absence of commercially deployed ATPs, this study proposes extracting truck platoons from real-world trajectory datasets to investigate the interactions between truck platoons and surrounding vehicles. A two-dimensional surrogate safety measure (SSM) known as Anticipated Collision Time (ACT) was employed to characterize these interactions. Bayesian Survival Analysis was developed to examine the interactions between truck platoons and adjacent passenger cars to provide some insights into how truck platoon might impact surrounding traffic. The results reveal that the position of the adjacent or right-leading truck in the platoon greatly influence the hazard of a minimum collision time. The increase of average time headway between trucks in platoon is found to shorten human drivers' responsiveness time to truck platoons. Moreover, the presence of a leading vehicle causes human drivers to reach the minimum collision time with truck platoons earlier, and this impact strengthens as the passenger car overtakes the truck platoon. These findings help us better understand the lateral interactions between truck platoon and adjacent passenger car, offering a theoretical foundation for traffic simulation involving heavy-duty truck platoons and recommendations for safety management of traffic flow involving truck platoons for future highways.